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A consistent multivariate test of association based on ranks of distances

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TLDR
In this paper, the problem of detecting associations between random vectors of any dimension is considered and a powerful test that is applicable in all dimensions and consistent against all alternatives is proposed. But the test has a simple form, is easy to implement, and has good power.
Abstract
SUMMARY We consider the problem of detecting associations between random vectors of any dimension. Few tests of independence exist that are consistent against all dependent alternatives. We propose a powerful test that is applicable in all dimensions and consistent against all alternatives. The test has a simple form, is easy to implement, and has good power.

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Kernel Angle Dependence Measures for Complex Objects

Yilin Zhang
TL;DR: In this paper , a nonlinear dependence measure based on the geometry element ''angle'' is proposed for data in metric space that can avoid the moment conditions to guarantee the distance is well defined.

A procedure to detect general association based on concentration of ranks.

TL;DR: RankCover as mentioned in this paper is a nonparametric association test of association between two variables that measures the concentration of paired ranked points using a disk-covering statistic similar to those employed in spatial data analysis.
Posted Content

Nonpar MANOVA via Independence Testing

TL;DR: In this paper, the authors prove that universally consistent independence tests achieve universally consistent $k$-sample testing and that energy and maximum mean discrepancy (MMD) are exactly equivalent to distance correlation (Dcorr) and Hilbert-Schmidt-Independence Criterion (Hsic).

Bayesian Methods in Analyzing the Association of Random Variables

Zichen Ma
TL;DR: This dissertation focuses on studying the association between random variables or random vectors from the Bayesian perspective by hypothesis testing for the independence among groups of random variables and modeling the dynamic association between two random variables given covariates.
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An interpretable semi‐supervised system for detecting cyberattacks using anomaly detection in industrial scenarios

TL;DR: In this article , the authors proposed an interpretable and semi-supervised system to detect cyberattacks in industrial settings, which was validated using data collected from the Tennessee Eastman Process.
References
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Journal Article

R: A language and environment for statistical computing.

R Core Team
- 01 Jan 2014 - 
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI

The Analysis of Variance

TL;DR: In this paper, the basic theory of analysis of variance by considering several different mathematical models is examined, including fixed-effects models with independent observations of equal variance and other models with different observations of variance.
Journal ArticleDOI

Measuring and testing dependence by correlation of distances

TL;DR: Distance correlation is a new measure of dependence between random vectors that is based on certain Euclidean distances between sample elements rather than sample moments, yet has a compact representation analogous to the classical covariance and correlation.
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Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations

TL;DR: 1. Density estimation for exploring data 2. D density estimation for inference 3. Nonparametric regression for explore data 4. Inference with nonparametric regressors 5. Checking parametric regression models 6. Comparing regression curves and surfaces
Journal ArticleDOI

The Analysis of Variance.

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